标题： 实验中的战略决策点：一种预测贝叶斯可选停止方法 显示英文标题

标题： Strategic decision points in experiments: A predictive Bayesian optional stopping method

摘要： 样本量的确定在实验设计中至关重要，特别是在交通和运输研究中。 频率学统计要求通过功效分析确定一个固定的样本量，一旦实验开始后就无法调整。 贝叶斯样本量确定方法，采用适当的先验分布，提供了一种替代方案。 贝叶斯可选停止（BOS）允许实验在达到统计目标时停止。 我们介绍了预测性贝叶斯可选停止（pBOS），结合BOS与贝叶斯预演模拟来预测未来数据，并在资源限制下如果目标难以实现时停止实验。 我们使用多元线性回归识别并纠正了预测中的偏差。 pBOS相比传统BOS具有高达118%的成本效益优势，并且比频率学方法更高效。 在特定条件下，pBOS允许研究人员在资源不足或收集到足够数据时停止实验，从而优化资源利用和成本节约。 显示英文摘要

摘要： Sample size determination is crucial in experimental design, especially in traffic and transport research. Frequentist statistics require a fixed sample size determined by power analysis, which cannot be adjusted once the experiment starts. Bayesian sample size determination, with proper priors, offers an alternative. Bayesian optional stopping (BOS) allows experiments to stop when statistical targets are met. We introduce predictive Bayesian optional stopping (pBOS), combining BOS with Bayesian rehearsal simulations to predict future data and stop experiments if targets are unlikely to be met within resource constraints. We identified and corrected a bias in predictions using multiple linear regression. pBOS shows up to 118% better cost benefit than traditional BOS and is more efficient than frequentist methods. pBOS allows researchers to, under certain conditions, stop experiments when resources are insufficient or when enough data is collected, optimizing resource use and cost savings.